Overview

Dataset statistics

Number of variables15
Number of observations645816
Missing cells0
Missing cells (%)0.0%
Duplicate rows90542
Duplicate rows (%)14.0%
Total size in memory73.9 MiB
Average record size in memory120.0 B

Variable types

Numeric9
Categorical6

Alerts

year has constant value "2019"Constant
month has constant value "11"Constant
Weekend has constant value "0"Constant
Dataset has 90542 (14.0%) duplicate rowsDuplicates
NumOfEventsInJourney is highly overall correlated with NumSessions and 1 other fieldsHigh correlation
NumSessions is highly overall correlated with NumOfEventsInJourney and 1 other fieldsHigh correlation
interactionTime is highly overall correlated with NumOfEventsInJourney and 1 other fieldsHigh correlation
maxPrice is highly overall correlated with minPriceHigh correlation
minPrice is highly overall correlated with maxPriceHigh correlation
NumCart is highly overall correlated with NumView and 1 other fieldsHigh correlation
NumView is highly overall correlated with NumCart and 1 other fieldsHigh correlation
InsessionCart is highly overall correlated with NumCart and 1 other fieldsHigh correlation
Purchase is highly imbalanced (89.4%)Imbalance
InsessionView is highly skewed (γ1 = 28.41525841)Skewed
interactionTime has 626461 (97.0%) zerosZeros
NumCart has 616828 (95.5%) zerosZeros
NumView has 33310 (5.2%) zerosZeros
InsessionCart has 613920 (95.1%) zerosZeros
InsessionView has 30664 (4.7%) zerosZeros

Reproduction

Analysis started2023-01-22 01:25:27.695003
Analysis finished2023-01-22 01:26:43.061911
Duration1 minute and 15.37 seconds
Software versionpandas-profiling vv3.6.2
Download configurationconfig.json

Variables

NumOfEventsInJourney
Real number (ℝ)

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0332339
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.9 MiB
2023-01-21T18:26:43.186480image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum12
Range11
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2018231
Coefficient of variation (CV)0.19533147
Kurtosis117.14257
Mean1.0332339
Median Absolute Deviation (MAD)0
Skewness8.1506696
Sum667279
Variance0.040732563
MonotonicityNot monotonic
2023-01-21T18:26:43.366507image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1 626424
97.0%
2 17733
 
2.7%
3 1394
 
0.2%
4 187
 
< 0.1%
5 48
 
< 0.1%
6 13
 
< 0.1%
7 6
 
< 0.1%
9 5
 
< 0.1%
8 4
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
1 626424
97.0%
2 17733
 
2.7%
3 1394
 
0.2%
4 187
 
< 0.1%
5 48
 
< 0.1%
6 13
 
< 0.1%
7 6
 
< 0.1%
8 4
 
< 0.1%
9 5
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
12 1
 
< 0.1%
10 1
 
< 0.1%
9 5
 
< 0.1%
8 4
 
< 0.1%
7 6
 
< 0.1%
6 13
 
< 0.1%
5 48
 
< 0.1%
4 187
 
< 0.1%
3 1394
 
0.2%
2 17733
2.7%

NumSessions
Real number (ℝ)

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0196976
Minimum1
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.9 MiB
2023-01-21T18:26:43.534215image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum11
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.15624732
Coefficient of variation (CV)0.15322908
Kurtosis213.14742
Mean1.0196976
Median Absolute Deviation (MAD)0
Skewness10.921405
Sum658537
Variance0.024413226
MonotonicityNot monotonic
2023-01-21T18:26:43.680474image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 634276
98.2%
2 10620
 
1.6%
3 760
 
0.1%
4 110
 
< 0.1%
5 27
 
< 0.1%
6 11
 
< 0.1%
8 4
 
< 0.1%
9 4
 
< 0.1%
7 3
 
< 0.1%
11 1
 
< 0.1%
ValueCountFrequency (%)
1 634276
98.2%
2 10620
 
1.6%
3 760
 
0.1%
4 110
 
< 0.1%
5 27
 
< 0.1%
6 11
 
< 0.1%
7 3
 
< 0.1%
8 4
 
< 0.1%
9 4
 
< 0.1%
11 1
 
< 0.1%
ValueCountFrequency (%)
11 1
 
< 0.1%
9 4
 
< 0.1%
8 4
 
< 0.1%
7 3
 
< 0.1%
6 11
 
< 0.1%
5 27
 
< 0.1%
4 110
 
< 0.1%
3 760
 
0.1%
2 10620
 
1.6%
1 634276
98.2%

interactionTime
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11987
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5817.8171
Minimum0
Maximum2526451
Zeros626461
Zeros (%)97.0%
Negative0
Negative (%)0.0%
Memory size4.9 MiB
2023-01-21T18:26:44.156944image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum2526451
Range2526451
Interquartile range (IQR)0

Descriptive statistics

Standard deviation71170.314
Coefficient of variation (CV)12.233165
Kurtosis353.21112
Mean5817.8171
Median Absolute Deviation (MAD)0
Skewness17.123669
Sum3.7572394 × 109
Variance5.0652136 × 109
MonotonicityNot monotonic
2023-01-21T18:26:44.600119image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 626461
97.0%
14 94
 
< 0.1%
16 83
 
< 0.1%
23 83
 
< 0.1%
22 81
 
< 0.1%
19 80
 
< 0.1%
11 76
 
< 0.1%
17 75
 
< 0.1%
8 75
 
< 0.1%
12 74
 
< 0.1%
Other values (11977) 18634
 
2.9%
ValueCountFrequency (%)
0 626461
97.0%
1 37
 
< 0.1%
2 35
 
< 0.1%
3 45
 
< 0.1%
4 63
 
< 0.1%
5 58
 
< 0.1%
6 45
 
< 0.1%
7 73
 
< 0.1%
8 75
 
< 0.1%
9 66
 
< 0.1%
ValueCountFrequency (%)
2526451 1
< 0.1%
2520440 1
< 0.1%
2508763 1
< 0.1%
2494377 1
< 0.1%
2478403 1
< 0.1%
2473321 1
< 0.1%
2442933 1
< 0.1%
2436952 1
< 0.1%
2433976 1
< 0.1%
2419162 1
< 0.1%

maxPrice
Real number (ℝ)

Distinct30870
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean291.46376
Minimum0
Maximum2574.07
Zeros1799
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size4.9 MiB
2023-01-21T18:26:44.939480image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile19.18
Q168.47
median164.48
Q3360.34
95-th percentile991.7825
Maximum2574.07
Range2574.07
Interquartile range (IQR)291.87

Descriptive statistics

Standard deviation356.3125
Coefficient of variation (CV)1.2224933
Kurtosis8.5677208
Mean291.46376
Median Absolute Deviation (MAD)116.09
Skewness2.5751827
Sum1.8823196 × 108
Variance126958.6
MonotonicityNot monotonic
2023-01-21T18:26:45.422104image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
154.42 2633
 
0.4%
89.84 2470
 
0.4%
334.37 2086
 
0.3%
231.64 1887
 
0.3%
0 1799
 
0.3%
308.63 1787
 
0.3%
643.23 1776
 
0.3%
308.86 1635
 
0.3%
51.46 1594
 
0.2%
82.63 1561
 
0.2%
Other values (30860) 626588
97.0%
ValueCountFrequency (%)
0 1799
0.3%
0.77 3
 
< 0.1%
0.79 3
 
< 0.1%
0.8 2
 
< 0.1%
0.81 3
 
< 0.1%
0.83 6
 
< 0.1%
0.85 5
 
< 0.1%
0.87 6
 
< 0.1%
0.88 20
 
< 0.1%
0.9 14
 
< 0.1%
ValueCountFrequency (%)
2574.07 73
< 0.1%
2574.04 114
< 0.1%
2573.99 12
 
< 0.1%
2573.81 51
< 0.1%
2573.79 121
< 0.1%
2573.76 1
 
< 0.1%
2573.45 5
 
< 0.1%
2573.29 2
 
< 0.1%
2573.17 1
 
< 0.1%
2572.23 104
< 0.1%

minPrice
Real number (ℝ)

Distinct30856
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean291.36393
Minimum0
Maximum2574.07
Zeros1813
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size4.9 MiB
2023-01-21T18:26:45.829604image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile19.13
Q168.47
median164.37
Q3360.34
95-th percentile991.13
Maximum2574.07
Range2574.07
Interquartile range (IQR)291.87

Descriptive statistics

Standard deviation356.21087
Coefficient of variation (CV)1.2225634
Kurtosis8.5741453
Mean291.36393
Median Absolute Deviation (MAD)115.98
Skewness2.5759157
Sum1.8816749 × 108
Variance126886.18
MonotonicityNot monotonic
2023-01-21T18:26:46.095403image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
154.42 2644
 
0.4%
89.84 2467
 
0.4%
334.37 2084
 
0.3%
231.64 1883
 
0.3%
0 1813
 
0.3%
308.63 1776
 
0.3%
643.23 1772
 
0.3%
308.86 1625
 
0.3%
51.46 1596
 
0.2%
82.63 1561
 
0.2%
Other values (30846) 626595
97.0%
ValueCountFrequency (%)
0 1813
0.3%
0.77 3
 
< 0.1%
0.79 3
 
< 0.1%
0.8 2
 
< 0.1%
0.81 3
 
< 0.1%
0.83 6
 
< 0.1%
0.85 5
 
< 0.1%
0.87 6
 
< 0.1%
0.88 20
 
< 0.1%
0.9 14
 
< 0.1%
ValueCountFrequency (%)
2574.07 73
< 0.1%
2574.04 113
< 0.1%
2573.99 12
 
< 0.1%
2573.81 51
< 0.1%
2573.79 121
< 0.1%
2573.76 1
 
< 0.1%
2573.45 5
 
< 0.1%
2573.29 2
 
< 0.1%
2573.17 1
 
< 0.1%
2572.23 104
< 0.1%

NumCart
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.04623608
Minimum0
Maximum9
Zeros616828
Zeros (%)95.5%
Negative0
Negative (%)0.0%
Memory size4.9 MiB
2023-01-21T18:26:46.328366image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum9
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.21738003
Coefficient of variation (CV)4.7015237
Kurtosis32.655611
Mean0.04623608
Median Absolute Deviation (MAD)0
Skewness5.0029997
Sum29860
Variance0.047254075
MonotonicityNot monotonic
2023-01-21T18:26:46.484735image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 616828
95.5%
1 28210
 
4.4%
2 710
 
0.1%
3 55
 
< 0.1%
4 7
 
< 0.1%
5 3
 
< 0.1%
9 1
 
< 0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
0 616828
95.5%
1 28210
 
4.4%
2 710
 
0.1%
3 55
 
< 0.1%
4 7
 
< 0.1%
5 3
 
< 0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
9 1
 
< 0.1%
7 1
 
< 0.1%
6 1
 
< 0.1%
5 3
 
< 0.1%
4 7
 
< 0.1%
3 55
 
< 0.1%
2 710
 
0.1%
1 28210
 
4.4%
0 616828
95.5%

NumView
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.97294121
Minimum0
Maximum9
Zeros33310
Zeros (%)5.2%
Negative0
Negative (%)0.0%
Memory size4.9 MiB
2023-01-21T18:26:46.628077image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q31
95-th percentile1
Maximum9
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.28325559
Coefficient of variation (CV)0.29113331
Kurtosis21.430125
Mean0.97294121
Median Absolute Deviation (MAD)0
Skewness-0.020164537
Sum628341
Variance0.080233732
MonotonicityNot monotonic
2023-01-21T18:26:46.789186image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 597891
92.6%
0 33310
 
5.2%
2 13613
 
2.1%
3 857
 
0.1%
4 106
 
< 0.1%
5 21
 
< 0.1%
6 10
 
< 0.1%
7 3
 
< 0.1%
9 3
 
< 0.1%
8 2
 
< 0.1%
ValueCountFrequency (%)
0 33310
 
5.2%
1 597891
92.6%
2 13613
 
2.1%
3 857
 
0.1%
4 106
 
< 0.1%
5 21
 
< 0.1%
6 10
 
< 0.1%
7 3
 
< 0.1%
8 2
 
< 0.1%
9 3
 
< 0.1%
ValueCountFrequency (%)
9 3
 
< 0.1%
8 2
 
< 0.1%
7 3
 
< 0.1%
6 10
 
< 0.1%
5 21
 
< 0.1%
4 106
 
< 0.1%
3 857
 
0.1%
2 13613
 
2.1%
1 597891
92.6%
0 33310
 
5.2%

InsessionCart
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.050811686
Minimum0
Maximum8
Zeros613920
Zeros (%)95.1%
Negative0
Negative (%)0.0%
Memory size4.9 MiB
2023-01-21T18:26:46.960493image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum8
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.22688883
Coefficient of variation (CV)4.4652884
Kurtosis26.380357
Mean0.050811686
Median Absolute Deviation (MAD)0
Skewness4.6647641
Sum32815
Variance0.051478542
MonotonicityNot monotonic
2023-01-21T18:26:47.111886image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 613920
95.1%
1 31065
 
4.8%
2 766
 
0.1%
3 52
 
< 0.1%
4 8
 
< 0.1%
5 3
 
< 0.1%
8 1
 
< 0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
0 613920
95.1%
1 31065
 
4.8%
2 766
 
0.1%
3 52
 
< 0.1%
4 8
 
< 0.1%
5 3
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
8 1
 
< 0.1%
7 1
 
< 0.1%
5 3
 
< 0.1%
4 8
 
< 0.1%
3 52
 
< 0.1%
2 766
 
0.1%
1 31065
 
4.8%
0 613920
95.1%

InsessionView
Real number (ℝ)

SKEWED  ZEROS 

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1981168
Minimum0
Maximum68
Zeros30664
Zeros (%)4.7%
Negative0
Negative (%)0.0%
Memory size4.9 MiB
2023-01-21T18:26:47.340491image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum68
Range68
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.87565668
Coefficient of variation (CV)0.73086086
Kurtosis1911.4005
Mean1.1981168
Median Absolute Deviation (MAD)0
Skewness28.415258
Sum773763
Variance0.76677462
MonotonicityNot monotonic
2023-01-21T18:26:47.533977image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1 500860
77.6%
2 87158
 
13.5%
0 30664
 
4.7%
3 18927
 
2.9%
4 5300
 
0.8%
5 1662
 
0.3%
6 686
 
0.1%
7 233
 
< 0.1%
8 78
 
< 0.1%
9 66
 
< 0.1%
Other values (11) 182
 
< 0.1%
ValueCountFrequency (%)
0 30664
 
4.7%
1 500860
77.6%
2 87158
 
13.5%
3 18927
 
2.9%
4 5300
 
0.8%
5 1662
 
0.3%
6 686
 
0.1%
7 233
 
< 0.1%
8 78
 
< 0.1%
9 66
 
< 0.1%
ValueCountFrequency (%)
68 34
< 0.1%
34 17
< 0.1%
32 21
< 0.1%
23 12
 
< 0.1%
22 14
< 0.1%
20 10
 
< 0.1%
19 21
< 0.1%
13 5
 
< 0.1%
12 17
< 0.1%
11 7
 
< 0.1%

year
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.9 MiB
2019
645816 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters2583264
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019
2nd row2019
3rd row2019
4th row2019
5th row2019

Common Values

ValueCountFrequency (%)
2019 645816
100.0%

Length

2023-01-21T18:26:47.727458image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-21T18:26:47.919785image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
2019 645816
100.0%

Most occurring characters

ValueCountFrequency (%)
2 645816
25.0%
0 645816
25.0%
1 645816
25.0%
9 645816
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2583264
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 645816
25.0%
0 645816
25.0%
1 645816
25.0%
9 645816
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2583264
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 645816
25.0%
0 645816
25.0%
1 645816
25.0%
9 645816
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2583264
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 645816
25.0%
0 645816
25.0%
1 645816
25.0%
9 645816
25.0%

month
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.9 MiB
11
645816 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1291632
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row11
2nd row11
3rd row11
4th row11
5th row11

Common Values

ValueCountFrequency (%)
11 645816
100.0%

Length

2023-01-21T18:26:48.099360image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-21T18:26:48.294916image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
11 645816
100.0%

Most occurring characters

ValueCountFrequency (%)
1 1291632
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1291632
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1291632
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1291632
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1291632
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1291632
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1291632
100.0%

weekday
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.9 MiB
Sat
126504 
Fr
123677 
Sun
110305 
Thu
78638 
Mon
71479 
Other values (2)
135213 

Length

Max length3
Median length3
Mean length2.808495
Min length2

Characters and Unicode

Total characters1813771
Distinct characters14
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSat
2nd rowFr
3rd rowThu
4th rowMon
5th rowTue

Common Values

ValueCountFrequency (%)
Sat 126504
19.6%
Fr 123677
19.2%
Sun 110305
17.1%
Thu 78638
12.2%
Mon 71479
11.1%
Tue 67850
10.5%
Wed 67363
10.4%

Length

2023-01-21T18:26:48.468004image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-21T18:26:48.710937image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
sat 126504
19.6%
fr 123677
19.2%
sun 110305
17.1%
thu 78638
12.2%
mon 71479
11.1%
tue 67850
10.5%
wed 67363
10.4%

Most occurring characters

ValueCountFrequency (%)
u 256793
14.2%
S 236809
13.1%
n 181784
10.0%
T 146488
8.1%
e 135213
7.5%
a 126504
7.0%
t 126504
7.0%
F 123677
6.8%
r 123677
6.8%
h 78638
 
4.3%
Other values (4) 277684
15.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1167955
64.4%
Uppercase Letter 645816
35.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
u 256793
22.0%
n 181784
15.6%
e 135213
11.6%
a 126504
10.8%
t 126504
10.8%
r 123677
10.6%
h 78638
 
6.7%
o 71479
 
6.1%
d 67363
 
5.8%
Uppercase Letter
ValueCountFrequency (%)
S 236809
36.7%
T 146488
22.7%
F 123677
19.2%
M 71479
 
11.1%
W 67363
 
10.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 1813771
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
u 256793
14.2%
S 236809
13.1%
n 181784
10.0%
T 146488
8.1%
e 135213
7.5%
a 126504
7.0%
t 126504
7.0%
F 123677
6.8%
r 123677
6.8%
h 78638
 
4.3%
Other values (4) 277684
15.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1813771
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
u 256793
14.2%
S 236809
13.1%
n 181784
10.0%
T 146488
8.1%
e 135213
7.5%
a 126504
7.0%
t 126504
7.0%
F 123677
6.8%
r 123677
6.8%
h 78638
 
4.3%
Other values (4) 277684
15.3%

timeOfDay
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.9 MiB
Afternoon
165214 
EarlyMorning
136248 
Evening
110578 
Morning
103407 
Dawn
80495 
Other values (2)
49874 

Length

Max length12
Median length9
Mean length7.9850995
Min length4

Characters and Unicode

Total characters5156905
Distinct characters19
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMorning
2nd rowDawn
3rd rowAfternoon
4th rowNoon
5th rowNoon

Common Values

ValueCountFrequency (%)
Afternoon 165214
25.6%
EarlyMorning 136248
21.1%
Evening 110578
17.1%
Morning 103407
16.0%
Dawn 80495
12.5%
Noon 34242
 
5.3%
Night 15632
 
2.4%

Length

2023-01-21T18:26:48.915060image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-21T18:26:49.149744image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
afternoon 165214
25.6%
earlymorning 136248
21.1%
evening 110578
17.1%
morning 103407
16.0%
dawn 80495
12.5%
noon 34242
 
5.3%
night 15632
 
2.4%

Most occurring characters

ValueCountFrequency (%)
n 1145631
22.2%
o 638567
12.4%
r 541117
10.5%
i 365865
 
7.1%
g 365865
 
7.1%
e 275792
 
5.3%
E 246826
 
4.8%
M 239655
 
4.6%
a 216743
 
4.2%
t 180846
 
3.5%
Other values (9) 939998
18.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4374841
84.8%
Uppercase Letter 782064
 
15.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 1145631
26.2%
o 638567
14.6%
r 541117
12.4%
i 365865
 
8.4%
g 365865
 
8.4%
e 275792
 
6.3%
a 216743
 
5.0%
t 180846
 
4.1%
f 165214
 
3.8%
l 136248
 
3.1%
Other values (4) 342953
 
7.8%
Uppercase Letter
ValueCountFrequency (%)
E 246826
31.6%
M 239655
30.6%
A 165214
21.1%
D 80495
 
10.3%
N 49874
 
6.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 5156905
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 1145631
22.2%
o 638567
12.4%
r 541117
10.5%
i 365865
 
7.1%
g 365865
 
7.1%
e 275792
 
5.3%
E 246826
 
4.8%
M 239655
 
4.6%
a 216743
 
4.2%
t 180846
 
3.5%
Other values (9) 939998
18.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5156905
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 1145631
22.2%
o 638567
12.4%
r 541117
10.5%
i 365865
 
7.1%
g 365865
 
7.1%
e 275792
 
5.3%
E 246826
 
4.8%
M 239655
 
4.6%
a 216743
 
4.2%
t 180846
 
3.5%
Other values (9) 939998
18.2%

Weekend
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.9 MiB
0
645816 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters645816
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 645816
100.0%

Length

2023-01-21T18:26:49.360399image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-21T18:26:49.543555image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 645816
100.0%

Most occurring characters

ValueCountFrequency (%)
0 645816
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 645816
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 645816
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 645816
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 645816
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 645816
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 645816
100.0%

Purchase
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.9 MiB
0
636839 
1
 
8977

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters645816
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 636839
98.6%
1 8977
 
1.4%

Length

2023-01-21T18:26:49.717163image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-21T18:26:49.919696image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 636839
98.6%
1 8977
 
1.4%

Most occurring characters

ValueCountFrequency (%)
0 636839
98.6%
1 8977
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 645816
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 636839
98.6%
1 8977
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
Common 645816
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 636839
98.6%
1 8977
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 645816
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 636839
98.6%
1 8977
 
1.4%

Interactions

2023-01-21T18:26:36.178441image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T18:26:07.358807image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T18:26:10.802366image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T18:26:14.255066image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T18:26:18.193130image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T18:26:22.524804image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T18:26:25.975227image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T18:26:29.547394image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T18:26:32.789900image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T18:26:36.547218image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T18:26:07.760111image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T18:26:11.120563image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T18:26:14.605649image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T18:26:18.685315image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T18:26:22.880047image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T18:26:26.377855image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T18:26:29.944283image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T18:26:33.148538image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T18:26:36.894176image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T18:26:08.129547image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T18:26:11.502096image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T18:26:14.967273image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T18:26:19.172952image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T18:26:23.347365image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T18:26:26.752650image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T18:26:30.292292image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T18:26:33.532093image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T18:26:37.259746image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T18:26:08.530685image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T18:26:11.867371image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T18:26:15.357667image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T18:26:19.599156image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T18:26:23.724503image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T18:26:27.116881image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T18:26:30.671477image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T18:26:33.915136image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T18:26:37.637537image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T18:26:08.875107image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T18:26:12.235194image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T18:26:15.967681image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T18:26:19.985231image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T18:26:24.103936image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T18:26:27.478810image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T18:26:31.023867image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T18:26:34.334267image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T18:26:38.021054image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T18:26:09.238738image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T18:26:12.595179image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T18:26:16.503943image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T18:26:20.477199image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T18:26:24.472030image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T18:26:27.851943image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T18:26:31.379223image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T18:26:34.714363image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T18:26:38.375053image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T18:26:09.599125image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T18:26:12.946663image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T18:26:16.973380image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T18:26:21.173781image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T18:26:24.844987image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T18:26:28.194902image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T18:26:31.712473image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T18:26:35.055882image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T18:26:38.709544image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T18:26:09.983127image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T18:26:13.531476image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T18:26:17.354820image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T18:26:21.724053image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T18:26:25.201421image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T18:26:28.561878image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T18:26:32.058525image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T18:26:35.398426image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T18:26:39.125664image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T18:26:10.392645image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T18:26:13.878193image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T18:26:17.732024image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T18:26:22.127674image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T18:26:25.619278image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T18:26:29.153092image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T18:26:32.443708image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T18:26:35.796197image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-01-21T18:26:50.099801image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
NumOfEventsInJourneyNumSessionsinteractionTimemaxPriceminPriceNumCartNumViewInsessionCartInsessionViewweekdaytimeOfDayPurchase
NumOfEventsInJourney1.0000.7680.9990.0250.0240.1540.4660.1490.1040.0020.0000.054
NumSessions0.7681.0000.7760.0310.0290.1090.3740.1060.0180.0020.0000.046
interactionTime0.9990.7761.0000.0260.0240.1520.4680.1470.1030.0100.0040.043
maxPrice0.0250.0310.0261.0001.0000.0050.0090.002-0.0220.0100.0080.014
minPrice0.0240.0290.0241.0001.0000.0040.0080.002-0.0220.0100.0080.014
NumCart0.1540.1090.1520.0050.0041.000-0.6790.951-0.3950.0170.0120.014
NumView0.4660.3740.4680.0090.008-0.6791.000-0.6450.4770.0160.0160.454
InsessionCart0.1490.1060.1470.0020.0020.951-0.6451.000-0.3680.0190.0110.002
InsessionView0.1040.0180.103-0.022-0.022-0.3950.477-0.3681.0000.0160.0110.001
weekday0.0020.0020.0100.0100.0100.0170.0160.0190.0161.0000.0390.037
timeOfDay0.0000.0000.0040.0080.0080.0120.0160.0110.0110.0391.0000.031
Purchase0.0540.0460.0430.0140.0140.0140.4540.0020.0010.0370.0311.000

Missing values

2023-01-21T18:26:39.603247image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-01-21T18:26:40.687279image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

NumOfEventsInJourneyNumSessionsinteractionTimemaxPriceminPriceNumCartNumViewInsessionCartInsessionViewyearmonthweekdaytimeOfDayWeekendPurchase
0110.0154.41154.410101201911SatMorning00
1110.092.6792.670101201911FrDawn00
2110.0155.71155.710101201911ThuAfternoon00
3110.0898.32898.320101201911MonNoon00
4110.0146.21146.210101201911TueNoon00
5110.0244.54244.540101201911SatNoon00
6110.0234.24234.240101201911SatMorning00
7110.0463.31463.310101201911WedEvening00
8110.0253.25253.250101201911ThuAfternoon00
9110.0450.18450.180101201911MonMorning00
NumOfEventsInJourneyNumSessionsinteractionTimemaxPriceminPriceNumCartNumViewInsessionCartInsessionViewyearmonthweekdaytimeOfDayWeekendPurchase
645806110.02239.182239.180101201911MonEarlyMorning00
645807110.0121.24121.240101201911SatDawn00
645808110.0615.20615.200101201911SatEvening00
645809110.02254.822254.820102201911SatMorning00
645810110.0991.06991.060101201911TueDawn00
645811110.0128.67128.670102201911SatEarlyMorning00
645812110.0244.51244.510101201911ThuEarlyMorning00
645813110.0152.82152.820101201911SunEarlyMorning00
645814110.0190.22190.220101201911WedEvening00
645815110.024.4124.410101201911WedAfternoon00

Duplicate rows

Most frequently occurring

NumOfEventsInJourneyNumSessionsinteractionTimemaxPriceminPriceNumCartNumViewInsessionCartInsessionViewyearmonthweekdaytimeOfDayWeekendPurchase# duplicates
42570110.0154.42154.420101201911FrAfternoon00138
83967110.0914.00914.000101201911SatEarlyMorning00123
56953110.0243.49243.490101201911SatEarlyMorning00122
36962110.0128.42128.420101201911SunEarlyMorning00119
36960110.0128.42128.420101201911SunAfternoon00113
42586110.0154.42154.420101201911SatEarlyMorning00111
83960110.0914.00914.000101201911FrDawn00110
42593110.0154.42154.420101201911SunEarlyMorning00107
36953110.0128.42128.420101201911SatAfternoon00106
27274110.089.8489.840101201911SatAfternoon00104